metadata
language: de
license: mit
tags:
- exbert
Overview
Language model: gbert-large-sts
Language: German
Training data: German STS benchmark train and dev set
Eval data: German STS benchmark test set
Infrastructure: 1x V100 GPU
Published: August 12th, 2021
Details
- We trained a gbert-large model on the task of estimating semantic similarity of German-language text pairs. The dataset is a machine-translated version of the STS benchmark, which is available here.
Hyperparameters
batch_size = 16
n_epochs = 4
warmup_ratio = 0.1
learning_rate = 2e-5
lr_schedule = LinearWarmup
Performance
Stay tuned... and watch out for new papers on arxiv.org ;)
Authors
- Julian Risch:
julian.risch [at] deepset.ai - Timo Möller:
timo.moeller [at] deepset.ai - Julian Gutsch:
julian.gutsch [at] deepset.ai - Malte Pietsch:
malte.pietsch [at] deepset.ai
About us
Our focus: Industry specific language models & large scale QA systems.
Some of our work:
- German BERT (aka "bert-base-german-cased")
- GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")
- FARM
- Haystack
Get in touch: Twitter | LinkedIn | Website
By the way: we're hiring!